Dust aerosol plays an important role in the climate system by
affecting the radiative and energy balances. Biases in dust modeling may
result in biases in simulating global energy budget and regional climate. It
is thus very important to understand how well dust is simulated in the
Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Here seven
CMIP5 models using interactive dust emission schemes are examined against
satellite-derived dust optical depth (DOD) during 2004–2016.

It is found that multi-model mean can largely capture the global spatial
pattern and zonal mean of DOD over land in present-day climatology in MAM and
JJA. Global mean land DOD is underestimated by −25.2 % in MAM to
−6.4 % in DJF. While seasonal cycle, magnitude, and spatial pattern are
generally captured by the multi-model mean over major dust source regions such as
North Africa and the Middle East, these variables are not so well represented
by most of the models in South Africa and Australia. Interannual variations
in DOD are not captured by most of the models or by the multi-model mean.
Models also do not capture the observed connections between DOD and local
controlling factors such as surface wind speed, bareness, and precipitation.
The constraints from surface bareness are largely underestimated while the
influences of surface wind and precipitation are overestimated.

Projections of DOD change in the late half of the 21st century under the
Representative Concentration Pathways 8.5 scenario in which the multi-model
mean is compared with that projected by a regression model. Despite the
uncertainties associated with both projections, results show some
similarities between the two, e.g., DOD pattern over North Africa in DJF and
JJA, an increase in DOD in the central Arabian Peninsula in all seasons, and
a decrease over northern China from MAM to SON.

Globally, the estimated radiative forcing from dust aerosol is 0.10 (−0.30
to +0.10) W m−2, a magnitude about one-fourth of the radiative
forcing of sulfate aerosol or black carbon from fossil fuel and biofuel
(Myhre et al., 2013; their Table 8.4).
Biases in dust simulation may potentially affect global energy budgets and
regional climate simulation. Thus, it is very important to examine the
capability of current state-of-the-art climate models in simulating dust.

Only a few studies examined the Coupled Model Intercomparison Project Phase
5 (CMIP5) model output of dust and most of them are regional evaluations.
For instance, Evan et al. (2014) examined model output for Africa,
but mainly focused on an area over the northeastern Atlantic (10–20∘ N and 20–30∘ W) where a long-term
proxy of dust optical depth (DOD) data over Cabo Verde islands is available
(Evan and Mukhopadhyay, 2010). They found models underestimated dust
emission and mass path and failed to capture the interannual variations from
1960 to 2004, as models did not capture the negative connection between dust
mass path and precipitation over the Sahel.

Another work examining CMIP5 aerosol optical depth (AOD) is by Sanap et
al. (2014) for India. They compared dust distribution in the models with the
Earth Probe Total Ozone Mapping Spectrometer (EPTOMS)/Ozone monitoring
Instrument (OMI) aerosol index (AI) from 2000 to 2005. They found most
CMIP5 models, except two HadGEM2 models, underestimated dust load over the
Indo-Gangetic Plains and suggested the biases are due to a misrepresentation
of 850 hPa winds in the models. Later, Misra et al. (2016) also examined
CMIP5-modeled AOD for India but did not specifically focus on dust.

Shindell et al. (2013) examined the output of 10 models from the
Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) for
1 year (2000), among which eight models also participated in the CMIP5.
They noticed that simulated dust AOD varies by more than a factor of 2
across models. However, this study also did not focus on dust but
emphasized the radiative forcings from anthropogenic aerosols.

None of the above studies examined global dust simulation in CMIP5 models.
What is more, most studies focused on annual mean, not seasonal averages. It
is very possible that models perform better in some seasons than others.
The AeroCom intercomparison among multiple dust models was performed on both global and
regional scales (Huneeus et al., 2011) but only focused on 1 year; thus the
models' capability of simulating interannual or long-term variability in
dust is not clear. A comprehensive evaluation of the climatology and
interannual variation in global DOD in CMIP5 models
will provide insights into models' capability of simulating the integrated
aerosol extinction due to dust, which is one of the key variables that
determine the radiative forcing of dust to the climate system.

Table 1CMIP5 models used in this study. Models tagged with plus signs (+)
included anthropogenic land use and land cover change in their vegetation
prediction.

Here we examine the results of seven CMIP5 models (Table 1) by comparing
model output with DOD derived from Moderate Resolution Imaging
Spectroradiometer (MODIS) Deep Blue aerosol products. Projections on changes
of DOD in the late half of the 21st century by CMIP5 models and also by a
regression model (Pu and Ginoux, 2017) are examined and analyzed. The
following section introduces data and methods used in this study. Results are
presented in Sect. 3, including examinations on the climatology and
interannual variations in CMIP5 DOD and future projections. Discussion and
major conclusions are presented in Sects. 4 and 5, respectively.

2.1 DOD from MODIS

DOD is a widely used variable that describes optical depth due to the
extinction by mineral particles. It is one of the key factors (single-scattering albedo and asymmetry factor being the two others) controlling
dust interaction with radiation. Monthly DOD values are derived from MODIS aerosol
products retrieved using the Deep Blue (MDB2) algorithm, which employs
radiance from the blue channels to detect aerosols globally over land even
over bright surfaces such as desert (Hsu et al., 2004, 2006).
Ginoux et al. (2012b) used collection 5.1 level 2 aerosol products from
MODIS aboard the Aqua satellite to derive DOD. Here, both MODIS aerosol
products (collection 6, level 2; Hsu et al., 2013) from the Aqua and
Terra platforms are used. Aerosol products such as AOD (550 nm), single-scattering albedo, and the Ångström exponent are first interpolated
to a regular 0.1∘ by 0.1∘ grid using the algorithm
described by Ginoux et al. (2010). The DOD is then derived from
AOD following the methods of Ginoux et al. (2012b) with adaptions for the
newly released MODIS collection 6 aerosol products (Pu and Ginoux,
2016). To separate dust from other aerosols, we use the Ångström
exponent (α) and single-scattering albedo (ω).
The Ångström exponent has been shown to be highly sensitive to particle
size (Eck et al., 1999). A continuous function relating the Ångström
exponent to fine-mode AOD established by Anderson et al. (2005;
their Eq. 5) based on ground-based data is used to separate dust from
fine particles. We also screen the data by setting single-scattering albedo
at 470 nm to be less than 1 for dust due to its absorption of solar
radiation. This separates dust from scattering aerosols such as sea salt,
which is purely scattering. The formula can be summarized as follows:

(1)DOD=AOD×(0.98-0.5089α+0.0512α2)if(ω<1).

Note that DOD represents the coarse-mode fraction of dust only. It is
estimated that the fine-mode dust at emission is less than 10 % (Kok et
al., 2017).

Aqua and Terra DOD values have previously been used to study global dust sources
(Ginoux et al., 2012b) and their geomorphological signature
(Baddock et al., 2016) as well as dust variations in the Middle East (Pu
and Ginoux, 2016) and the US (Pu and Ginoux, 2017), and they have been
validated with AErosol RObotic NETwork (AERONET) stations over the US
(Pu and Ginoux, 2017). Here we compare Aqua and Terra DOD against
AERONET stations globally (Sect. S1 and Figs. S1, S2 in the Supplement).
Both Aqua and Terra DOD values are slightly underestimated, with respective errors
of 0.08+0.52 DOD and 0.10+0.48 DOD.

Daily DOD from Aqua and Terra is averaged to monthly data and interpolated
to a 1∘ by 1∘ grid. Terra passes the Equator from north
to south around 10:30 LT while Aqua passes the Equator from south to
north around 13:30 LT. To reduce missing data and also to combine
the information from both morning and afternoon hours, a combined monthly
DOD (here after MODIS DOD) is derived by averaging Aqua and Terra DOD when
both products are available or using either Aqua or Terra DOD when only one product
is available. As shown in Fig. S3, the mean available
days in each season and also spatial coverage are more enhanced in combined DOD
than using Aqua or Terra (not shown) DOD alone. This combined DOD is
available from January 2003 to December 2016.

Table 2List of regions selected to compare model output with MODIS DOD.
Locations of these regions are also plotted in Fig. 1b. Acronyms are used
for some regions for short, and are listed in the brackets in the first
column. Note that the region names such as northern China and India are not
exactly the same as their geographical definitions but also cover some areas
from nearby countries.

CALIOP measures backscattered radiances attenuated by the presence of
aerosols and clouds and retrieves corresponding microphysical and optical
properties of aerosols. Monthly dust AOD (or DOD) measurements on a 2∘ latitude by
5∘ longitude grid are available since June 2006. The climatology of
CALIOP DOD during 2007–2016 is similar to that of MODIS DOD during the same
period (Fig. S5). The global mean (over land) MODIS DOD is slightly higher
than that from CALIOP, probably due to the lower horizontal resolution of the
latter. The pattern correlations (e.g., Pu et al., 2016) between the two
products range from 0.83 in boreal spring and summer to 0.63 in boreal winter
(Fig. S5). Due to higher spatial resolution (compared with CALIOP) and
coverage (compared with AERONET sites), MODIS DOD is chosen as the primary
product to validate CMIP5 model output. Nine regions (Table 2) are selected
to study the DOD magnitude, spatial pattern, and variations. These regions
cover major dust source regions previously identified (Ginoux et al., 2012).

Given the analysis above (Figs. S3–S5), there are some uncertainties
associated with DOD in a few regions in some seasons: (1) relatively low
coverage (<30 days per season) over northern China and southeastern
Asia in JJA, (2) DOD slightly higher than COD from AERONET over the
Arabian Peninsula in DJF and SON, and (3) DOD lower than CALIOP over northern
India in MAM. We will consider these uncertainties in the following analysis
wherever they are relevant.

2.2 Reanalysis and observation datasets

Previous studies have found that the variations in dust event frequency over the US
in the recent decade could be largely represented by the variations in three
local controlling factors: seasonal mean surface wind speed, bareness, and
precipitation (Pu and Ginoux, 2017). These factors have previously been found
to constrain dust emission or variability on multiple timescales (e.g.,
Gillette and Passi, 1988; Fecan et al., 1999; Zender and Kwon, 2005). While
surface wind is positively related to the emission and transport of dust,
vegetation is an important non-erodible element that prevents wind erosion.
Precipitation is generally negatively related to dust emission and transport
processes. While the scavenging effect of precipitation on small dust
particles only lasts a few hours or days, influences of precipitation on soil
moisture lasts longer.

To examine the interannual variations in DOD and its connection with local
controlling factors such as surface wind speed, bareness, and precipitation,
monthly data of 10 m wind speed from the ERA-Interim (Dee et al., 2011),
leaf area index (LAI) data from Advanced Very High Resolution Radiometer
(AVHRR; Claverie et al., 2014, 2016) and precipitation from
the Precipitation Reconstruction over Land (PRECL; Chen et
al., 2002) are used.

ERA-Interim is a global reanalysis from the European Centre for Medium-Range
Weather Forecasts (ECMWF). Its horizontal resolution is T255 (about
0.75∘ or 80 km). We choose this analysis because of its relatively
high spatial resolution. The monthly data are available from 1979 to present
day.

Monthly LAI derived from version 4 of the Climate Data Record (CDR) of AVHRR
is used to calculate surface bareness. The data are produced by the National
Aeronautics and Space Administration (NASA) Goddard Space Flight Center
(GSFC) and the University of Maryland. Monthly gridded data at a horizontal
resolution of 0.05∘ by 0.05∘ are available from 1981 to
present. This product is selected due to its high spatial resolution and long
temporal coverage. Surface bareness is calculated from seasonal mean LAI (Pu
and Ginoux, 2017) as the following:

(2)bareness=exp(-1×LAI).

Bareness is originally defined as exp (−LAI − SAI), where SAI is stem area index (Evans et
al., 2016). Since the satellite does not retrieve brownish SAI, we only use LAI to
calculate bareness.

PRECL precipitation from the National Oceanic and Atmospheric Administration
(NOAA) is a global analysis available monthly from 1948 to present at a
1∘ by 1∘ resolution. The dataset is derived from gauge
observations from the Global Historical Climatology Network (GHCN), version
2, and the Climate Anomaly Monitoring System (CAMS) datasets. Its long
coverage and spatial resolution are suitable to study the connections between
DOD and precipitation.

2.3 CMIP5 model output

Among CMIP5 models we selected seven models (Table 1) that used interactive
dust emission schemes, in which dust emission varied in response to changes
of climate. The outputs of 10 m wind speed, precipitation, and LAI are also
available from these models. In models in which dust is simulated offline, i.e.,
dust emission did not interactively respond to meteorological and climate
changes, the connections between DOD and modeled controlling factors are
lost. Other models (to the best of our knowledge) either used offline dust as an
input or did not write out the variables needed for this analysis.

Both the historical run from 1861 to 2005 and the future run under the
Representative Concentration Pathways 8.5 (RCP8.5) scenario
(Riahi et al., 2011) from 2006 to 2100 are used. Here the RCP8.5 scenario is chosen because it represents the upper limit of the
projected greenhouse gas change in the 21st century and thus likely
is the worst-case scenario for future DOD variation under climate change.
Also, studies found that the observed CO2 emission pathway during 2005–2014
matches the RCP8.5 scenario better than other scenarios (e.g., Fuss et al.,
2014), which makes the RCP8.5 output suitable to examine present-day DOD
variations after 2005.

Monthly model outputs of dust load, surface 10 m wind speed, precipitation,
and LAI are used. Historical output from 2003 to 2005 and RCP8.5 output
from 2006 to 2016 are combined to form time series and climatology during
2003–2016 to compare with MODIS DOD during the same time period.

2.3.1 DOD derived from modeled dust load

Most CMIP5 models did not save DOD, so we used monthly dust load and
converted it to DOD using the relationship derived by Ginoux et al. (2012a) as
follows:

(3)τ=M×e,

where τ is DOD at 500 nm, M is the load of dust in
grams per square meter, and e=0.6 m2 g−1 is the mass extinction
efficiency. Dust load from different models is first interpolated to a
2∘ by 2.5∘ grid and then converted to DOD. The same method
was used by Pu and Ginoux (2017) for the US. Applying the same mass
extinction efficiency everywhere and to all the CMIP5 model outputs used here
is a simplification, as different models may have quite different mass
extinction efficiency. For instance, e can range from 0.25 to
1.28 m2 g−1 in AeroCom models, with a multi-model medium of
0.72 m2 g−1 (Huneeus et al., 2011). Here, we compare the derived
DOD with modeled DOD from one historical simulation of the GFDL-CM3 model (Donner
et al., 2011) as an example. A full validation of this method will require
modeled DOD from all the other CMIP5 models, which are currently not
available. The pattern correlations of the climatology (1861–2005) between
the derived DOD and modeled DOD in GFDL-CM3 are very high, all above 0.99 for
four seasons (not shown). The percentage differences between derived DOD and
modeled DOD averaged over global land range from −3.6 % in DJF and SON
to 1.3 % in MAM and JJA.

2.4 A linear regression model

2.4.1 Multiple linear regression

In order to examine the relative contribution of each local controlling
factor to DOD variations, multiple linear regressions are applied by
regressing MODIS DOD onto standardized seasonal mean ERA-Interim surface
wind speed, AVHRR bareness, and PRECL precipitation at each grid point. All
the data are re-gridded to a 1∘ by 1∘ grid before the
calculation. Over regions where values are missing for any of the
explanatory variables (i.e., precipitation, bareness, and surface wind
speed) or DOD, the regression coefficients are set to missing values. The
collinearity among these explanatory variables is examined by calculating the
variance inflation factor (VIF) (e.g., O'Brien, 2007; Abudu et al.,
2011), and in most regions the VIF is below 2 (not shown), indicating a low
collinearity (5–10 is usually considered high). Bootstrap resampling is
used to test the significance of the regression coefficients, following the
method used by Pu and Ginoux (2017).

Multiple linear regression is also applied to CMIP5-model-derived DOD and
outputs of surface wind speed, bareness, and precipitation to obtain
regression coefficients from the models from 2004 to 2016. All variables are
interpolated to a 2∘ by 2.5∘ grid before regression. The
results are compared with regression coefficients derived from observational
datasets.

2.4.2 DOD reconstruction and future projection

Using regression coefficients obtained from observations and observed
variations in precipitation, bareness, and surface wind speed from 2004 to
2016, we can reconstruct DOD in the present day and compare it with MODIS
DOD (see discussion in Sect. 3.2).

Similar to the method used by Pu and Ginoux (2017), the regression
coefficients derived from MODIS DOD and observed controlling factors and
CMIP5 model output of surface wind speed, bareness, and precipitation are
used to project variations in future DOD. The regression coefficients are
interpolated from the 1∘ by 1∘ grid to a 2∘ by
2.5∘ grid to be consistent with model output. Such an
interpolation may smooth out some spatial characteristics from observations.
Here we tried two groups of CMIP5 output for these controlling factors. One
group used seven models with an interactive dust emission scheme (Table 1),
and the other used 16 CMIP5 models (see Supplement Table S1 of Pu and
Ginoux, 2017) that include the seven models with the interactive dust emission
scheme. The reason to test the latter is to include as much model output of
the controlling factors as possible. The differences between the historical
run (1861–2005 average) and that of the RCP8.5 run for the late half of
the 21st century (2051–2100) are standardized by the standard
deviation of the historical run for each explanatory variable. The projected
change reveals how DOD will vary with reference to the historical conditions
(mean and standard deviation).

Figure 1Climatology (2004–2016) of Aqua and Terra combined DOD
(i.e., MODIS DOD; a–d) and multi-model mean of CMIP5 DOD (e–h) for
four seasons. The pattern correlations (centered; calculated after
interpolating MODIS DOD to CMIP5 DOD grids) between CMIP5 and MODIS DOD are
shown in pink in the bottom panel. Blue numbers denote global mean DOD over
land. For CMIP5 model results, ± 1 standard deviation among seven
CMIP5 models is also shown. Black boxes in (b) denote nine averaging regions
(Table 2). Here we only added these boxes in (b) instead of every plot to
keep the figure clean. Note that CMIP5 multi-model mean is masked by MODIS
DOD for comparison. The dotted area in (e–h) shows where multi-model mean is
greater than 1 inter-model standard deviation.

3.1 Climatology (2004–2016)

Figure 1 shows the climatology of MODIS DOD (Fig. 1a–d) in four seasons
during 2004–2016 and that from the CMIP5 multi-model mean (Fig. 1e–h).
Globally, the dustiest regions are largely located over the Northern
Hemisphere (NH) over North Africa, the Middle East, and East Asia
(Fig. 1a–d). In these regions, DOD is higher in boreal spring and summer
than fall and winter. Modeled global DOD over land is generally lower than
that from MODIS DOD, ranging from −0.028 (−25.2 %) in MAM to −0.005
(−6.4 %) in DJF. The global spatial pattern is better captured in MAM
and JJA, with pattern correlations of 0.74 and 0.85, respectively
(Fig. 1f–g). In DJF, DOD is overestimated over central Africa and Australia
but underestimated over the Middle East and Asia (Fig. 1e) while in SON
there is a similar overestimation in Australia and an underestimation in the
Middle East (Fig. 1h).

Figure 2 shows the zonal mean of CMIP5 DOD from individual models (thin
colorful lines) and multi-model ensemble mean (thick black), in comparison
with MODIS DOD (thick red). In DJF, DOD is underestimated in the NH from 15
to 50∘ N but overestimated over the tropics and Southern Hemisphere
(SH) (Fig. 2a). While the overestimation in the SH is largely contributed by
three models, the underestimation in the NH appears in all seven models.
The overestimation of DOD in HadGEM2-ES has also been identified in a
previous study (Bellouin et al., 2011) and will be discussed later. In MAM, a
similar overestimation of DOD in the tropics and SH also occurs in some
models, and the multi-model mean slightly overestimates DOD around
20–30∘ S (Fig. 2b). In the NH, there is a weak underestimation too, but
the overall gradient is largely captured. In JJA, the multi-model mean
resembles MODIS DOD very well (Fig. 2c), consistent with the highest pattern
correlation in this season shown in Fig. 1. The peak around 19∘ N in
North Africa and the Middle East is well captured by the multi-model mean,
although the magnitude is slightly underestimated. In SON, different from
MODIS DOD that peaks around 19∘ N, the multi-model mean has two
peaks around 15∘ N and 28∘ S, a pattern
somewhat similar to that in DJF (Fig. 2d). Consequently, DOD in CMIP5
multi-model mean is overestimated at 15–40∘ S and 0–15∘ N
but underestimated at 0–15∘ S and
15–40∘ N.

Seasonal cycles of CMIP5 DOD are compared with MODIS DOD in nine regions in
Fig. 3. The annual means of DOD in each region from multi-model mean
(black) and MODIS (red) are also listed in each plot. The spread of DOD
among individual models is greater during boreal spring and summer for
regions in the NH and during austral spring and summer for regions in the
SH. Seasonal cycles over North Africa, the Middle East, North America, and
India are generally captured by multi-model mean, with modeled DOD peaking
during the same seasons as MODIS DOD (Fig. 3a–e). While some models
overestimate the seasonal peaks over the Middle East, North America, and
India (e.g., CanESM2, HadGEM2-ES, and HadGEM2-CC), a few models have very
weak seasonal cycles and underestimate DOD over North America and India
(e.g., GFDL-CM3, NorESM1-M, MIROC-ESM, and MIROC-ESM-CHEM). Note that MODIS
DOD is slightly lower than CALIOP DOD over India in MAM (Fig. S5); therefore
for these models the underestimation may be larger than shown in Fig. 3e.

Since the temporal coverage of MODIS DOD over northern China and
southeastern Asia is relatively low in JJA compared with other regions (Fig. S3),
we also examined the seasonal cycle of CALIOP DOD (not shown) and
results are similar but with weaker magnitude. Over northern China, MODIS
DOD peaks in spring (Fig. 3c), consistent with previous studies (e.g.,
Zhao et al., 2006; Laurent et al., 2006; Ginoux et al., 2012b),
while multi-model mean peaks later in May–June. Individual models have quite
different seasonal cycles, with the GFDL-CM3 model having a peak (in April)
closer to the timing of the MODIS maximum. Similar misrepresentation occurs over
the southeastern Asia (Fig. 3f).

In South Africa and South America the observed maxima in early austral
spring (i.e., September) are also not captured by the multi-model mean
(Fig. 3g–h). Note that CanESM2 largely captures the seasonal cycle of DOD
over South America, although the magnitude is overestimated (Fig. 3h). In
Australia, DOD is largely overestimated and the peak from November to
January in MODIS DOD is shifted about 1 month earlier in the multi-model
mean (Fig. 3i). Similar to the finding here, Bellouin et al. (2011) also
found that the HadGEM2-ES model overestimated DOD over Australia and the Thar Desert
region in northwestern India and suggested that these overestimations were
likely due to the model's overestimation of bare soil fraction and
underestimation of soil moisture. Despite overestimation, the seasonal cycle
in the HadGEM2-CC model is more similar to MODIS DOD than other models (Fig. 3i).

Figure 4Spatial statistics comparing DOD from CMIP5 models with that from
MODIS in nine regions. Label on the x axis shows individual models (1–7)
and multi-model mean (8). y axis shows the ratio of pattern standard
deviations between model climatology (2004–2016) and that of MODIS, which
reveals the relative amplitude of the simulated DOD versus satellite DOD. The
color denotes pattern correlation (centered) between each model and MODIS DOD
in each region.

We further examine the magnitudes and spatial patterns of CMIP5 DOD in these
regions. Figure 4 shows the ratio of pattern standard deviations (standard
deviations of values within the domain) and pattern correlation between
CMIP5 DOD and MODIS DOD climatology (2004–2016) in each region for four
seasons. While the former reveals the magnitude differences, the latter
demonstrates the spatial resemblance.

Over North Africa, the Middle East, and India, the ratio of CMIP5 DOD from
individual models and multi-model mean versus MODIS DOD are all within ±
1 order of magnitude (Fig. 4). Most models underestimate DOD in northern
China, although the magnitudes are largely within the range of −1 order
of magnitude to 1. Over North America, South Africa, and Australia, some
models underestimate the DOD by more than 2 orders of magnitude, while
over Australia three models overestimate DOD by more than 1 order of
magnitude. In general, magnitudes of multi-model mean are closer to satellite
DOD than most individual models and are largely within ± 1 order of
magnitude of MODIS DOD.

The spatial patterns are better captured over North Africa and the Middle
East than other regions (Fig. 4), with pattern correlations above 0.6 in
most models (with the highest pattern correlations of 0.92 and 0.83). Pattern correlations from multi-model mean are also high,
reaching 0.87 (0.78) over North Africa and 0.75 (0.73) over the Middle East
in JJA (MAM). Nonetheless, some models show negative pattern correlations
over North Africa, northern China, North America, southeastern Asia, South
Africa, South America, and Australia. Overall, spatial patterns are less
well represented in regions over the SH than over the NH in CMIP5 models.

In short, in terms of both magnitudes and spatial pattern, DOD climatology
is best represented over North Africa and the Middle East among the nine
regions. The multi-model mean shows that DOD over North Africa is slightly
better simulated than over the Middle East, somewhat similar to the finding
of the AeroCom multi-model analysis (Huneeus et al., 2011).

3.2 Interannual variations

An important aspect of dust activity is its long-term variability, including
interannual and decadal variations. Dust emission in North Africa is known
to have strong decadal variations (e.g., Prospero and Nees, 1986;
Prospero and Lamb, 2003; Mahowald et al., 2010; Evan et al., 2014, 2016),
while over Australia, strong interannual variations have been
related to El Niño–Southern Oscillation (e.g., Marx et al., 2009;
Evans et al., 2016). Due to the short time coverage of high-quality
satellite products, we focus on interannual variations in DOD from 2004 to
2016.

Figure 5 shows the correlations of regional mean time series of DOD between
MODIS and CMIP5 models and multi-model mean for each season in nine regions.
We also show correlations between the reconstructed DOD (see Sect. 2.4.2
for details) and MODIS DOD for reference (Table S1). The
reconstructed DOD is calculated using observed regression coefficients and
time-varying controlling factors from observations (i.e., surface wind
speed, bareness, and precipitation).

Figure 5Correlations (color) between regional averaged time series from
CMIP5 DOD and MODIS DOD from 2004 to 2016 for four seasons. Numbers on the
x axis denote each model (1–7) and multi-model mean (8). Correlations
significant at the 90 % confidence level are marked by a star and
significance at the 95 % confidence level by two stars.

The interannual variations in DOD are in general not well captured by CMIP5
models. This is consistent with a previous study by Evan et al. (2014), who
found dust variability downwind of North Africa over the northeastern
Atlantic was misrepresented in CMIP5 models. In most regions, only one or
two models show significant positive correlation with MODIS DOD in some
seasons, and negative correlations exist in all regions (Fig. 5). North
Africa, the Middle East, southeastern Asia, South America, and Australia
show fewer negative correlations than other dusty regions. Conversely,
reconstructed DOD shows significant positive correlations with MODIS DOD
over most regions in all seasons (Table S1). This suggests
that the interannual variations in DOD can be largely attributed to the
variations in these controlling factors, and models may misrepresent these
relationships, in addition to their incapacity to capture the interannual
variations in individual controlling factors in general (not shown), which
is not uncommon for coupled models.

We further examine the connection between those controlling factors and DOD
in CMIP5 models. Figure 6 shows the dominant controlling factors among the
three (surface wind speed, bareness, and precipitation) on DOD variations in
four seasons from MODIS (left column) and from CMIP5 multi-model mean (right
column). To highlight factors controlling DOD variations near
the dust source regions, a mask of AVHRR LAI ≤0.5 is applied to both
coefficients.

Figure 6Regression coefficients calculated by regressing DOD in each
season onto standardized precipitation (purple), bareness (orange), and
surface wind speed (green) from 2004 to 2016. Coefficients obtained using
MODIS DOD and observed controlling factors (interpolated to a 2∘
by 2.5∘ grid) and those using CMIP5 multi-model mean DOD and
controlling factors are shown in the left and right columns, respectively.
The color of the shading denotes the largest coefficient in absolute value
among the three, while the saturation of the color shows the magnitude of
the coefficient (from 0 to 0.02). Only regression coefficients significant
at the 90 % confidence level (bootstrap test) are shown. Missing values
are shaded in grey. To highlight coefficients near the source regions, a
mask of LAI ≤0.5 is applied.

Bareness plays the most important role in many dusty regions in
observations, e.g., over Australia, the central US, and South America (Fig. 6a–d).
Note that while bareness plays an important role over the Sahel
during DJF and MAM, it also shows strong signal over some areas in
northern North Africa (Fig. 6a–b). The reliability of this information is
limited by the accuracy of LAI retrieval in these areas. The value of
bareness in this region is actually quite high (as LAI is very low), but
still has weak interannual variability (Fig. S6). Over
some areas of North and South Africa, the Middle East, and East Asia,
surface wind and precipitation are also quite important.

The role of bareness is largely underestimated in CMIP5 models while
surface wind and precipitation become the dominant factors (Fig. 6e–h). The
misrepresentation of the connection between DOD and these controlling
factors may cause the misrepresentation of the dust load and its
variability. Taking Australia for an example, the overestimation of DOD
magnitudes may be related to an overestimation of the influence of surface
wind on DOD and a lack of constraints from surface bareness.

Despite the large differences between the observed and modeled connections
between DOD and the controlling factors, some regions show similarities. For
instance, over North Africa in DJF, both show an important influence from
surface winds (Fig. 6a, e), although the locations of surface-wind-dominant
areas are not exactly the same. Evan et al. (2016) also found a
dominant role of surface wind in African dust variability, but they focused
on monthly means not seasonal averages. In MAM, precipitation starts to
play a role in some parts of North Africa while surface wind still
dominates in some areas (Fig. 6b). The same increasing influence of
precipitation is shown in the multi-model mean, but such an influence seems
overestimated (Fig. 6f). In JJA, the influences of precipitation and
bareness over the eastern Arabian Peninsula in the multi-model mean (Fig. 6g)
also show some similarity to observation (Fig. 6c), although an
underestimation of the influence from bareness and an overestimation of
precipitation are still there.

Also, note that in CMIP5 models, due to a lack of constraints from low surface
temperature (e.g., over frozen land) and snow cover on dust emission or
misrepresentations of dust transport, DOD and also the regression
coefficients still exist over NH high latitudes in boreal winter and spring
in the multi-model mean (Fig. 6e–f).

Figure 7Projected changes of DOD in the late half of the 21st century
(under the RCP8.5 scenario) from that in the historical level (1861–2005)
by the CMIP5 multi-model mean for four seasons. The percentage change of global
mean (over land) DOD ± 1 inter-model standard deviation is shown at
the bottom of each plot. Areas with sign agreement among the models reaching
71.4 % (i.e., at least five out of seven models have the same sign as the
multi-model mean) are dotted.

Table 3Changes of DOD in the late half of the 21st century (2051–2100;
RCP8.5 scenario) from the historical condition (1861–2005) projected by
the CMIP5 multi-model mean (second to fifth columns) and the regression model
(sixth to ninth columns) in nine regions. Changes of DOD are shown as a
percentage with reference to CMIP5 multi-model historical run. Note that in
some regions the projected change by the regression model is quite large
(i.e., greater than ±100 %), largely due to the underestimation of
CMIP5 historical run in these regions.

3.3 Future projections

How will DOD change in response to increasing greenhouse gases? The results
from the CMIP5 multi-model mean are shown in Fig. 7. We compare the DOD during
the late half of the 21st century under the RCP8.5 scenario with that
in the historical level (1861–2005 average).

Over land, the CMIP5 model projects a decrease in global mean DOD in all seasons
except JJA (Fig. 7a–d). The inter-model standard deviation is much greater
than the multi-model mean, suggesting large discrepancies among individual
models. The projected decrease is largely over northern North America,
southern North Africa, eastern central Africa, and East Asia while the
increase is largely over northern North Africa, the Middle East, southern
North America, South Africa, South America, and southern Australia (Fig. 7).
Regional means of DOD change (as a percentage) with reference to the CMIP5
historical run are summarized in Table 3.

Figure 8Projected difference of (a–d) precipitation
(mm d−1), (e–h) bareness, and (i–l) 10 m wind
(m s−1) between the late half of the 21st century (2051–2100; RCP8.5
scenario) and historical levels (1861–2005) from the multi-model mean of seven
CMIP5 models. Areas with sign agreement among the models reaching 71.4 %
(i.e., at least five out of seven models have the same sign as the
multi-model mean) are dotted.

What might be the causes of DOD change? Figure 8 shows the projected change
of precipitation, bareness, and surface wind speed from CMIP5 multi-model
mean. These factors play an important role in DOD variations in the present
day, although models tend to underestimate the role of bareness and
overestimate the influences of precipitation and surface wind (Fig. 6).
Increases in precipitation can increase soil moisture and remove airborne
dust, thus usually favoring a decrease in DOD. As shown in Fig. 8a–d, the
increases in precipitation in northern Eurasia, northern North America, the
Congo basin in Africa, and Australia (DJF and MAM) may contribute to the
decrease in DOD in these regions, while the decreases in precipitation over
northern North Africa and the Middle East (DJF and MAM), South Africa, and
South America may contribute to the increase in DOD (DJF-SON). Also note
that in JJA both precipitation and DOD increase over northern North Africa
and the Middle East (Fig. 8c), suggesting other factors dominate the
variation in DOD in the multi-model mean.

A decrease (increase) in bareness indicates a growth (decay) of vegetation
and is usually associated with a decrease (increase) in DOD. In general,
except regions such as southern North America, South America, South Africa,
part of northern Eurasia, and central Sahel, the pattern of bareness change
does not resemble DOD change (Fig. 8e–h). This is probably due to the fact
that the overall influence of bareness on DOD variation is underestimated in
CMIP5 models (Fig. 6).

Increases in surface wind can enhance dust emission and transport, and vice
versa. The changes of surface wind in DJF and MAM are similar and likely to
contribute to the increase in DOD over northern North Africa, the Middle
East, eastern South America, southern South Africa, and southern Australia
(Fig. 8i–j). The decrease in DOD over northwestern North America, the
Sahel, and northern Australia may also relate to the decrease in surface
wind there, in addition to an increase in precipitation and a reduction of
bareness. In JJA and SON (Fig. 8k–l), the increases in surface wind in
South America, South Africa, and central Australia and the decreases in wind in
northwestern North America, northern Eurasia, and the central Sahel are also
consistent with patterns of DOD change.

In short, variations in CMIP5 DOD in the late half of the 21st century are
more consistent with changes of precipitation and surface wind speed than
with surface bareness, consistent with the analysis above regarding the
present-day condition.

Figure 9Projected change of DOD in the late half of the 21st century under
the RCP8.5 scenario by the regression model. The results are calculated
using the regression coefficients obtained from observations during
2004–2016 (see Sect. 2) and projected changes of precipitation,
bareness, and surface wind from seven CMIP5 models. Dotted areas are regions
with sign agreement among the regression projections (using output of each
of the seven models) above 71.4 % (i.e., at least five out of seven
regression projections have the same sign as the multi-model mean
projection). To highlight DOD variations near the source regions, a mask of
LAI ≤0.5 (from present-day climatology) is applied.

Here we also present the projected change of DOD from the regression model
in Fig. 9. The regression model (see Sect. 2.4 for details) is developed
based on observed relationships between MODIS DOD and local controlling
factors and can largely capture the interannual variations in DOD in the
present-day climate (Table S1). Assuming that the observed
connection between DOD and these controlling factors does not change
dramatically in the future, we can use this regression model and CMIP5-model-projected change of controlling factors to project DOD variations. Compared
to DOD projection from CMIP5 models, this approach additionally utilizes
observational constraints and is likely to provide a more reliable future
projection. We use projected changes of precipitation, bareness, and surface
wind speed from seven CMIP5 models with an interactive dust emission scheme
(see methodology). A similar method is applied to the model output from 16
CMIP5 models, and results are similar (Fig. S7). A mask
of present-day LAI ≤0.5 is also applied to highlight the changes of
DOD near dust source regions. By doing this, we assume the location of major
dust sources will not change much in the late half of the 21st century.
The unmasked figure is presented in the Supplement (Fig. S8). The reason
we did not use the projected future LAI as a mask is
that there are large uncertainties associated with LAI projection, especially
over NH subtropical regions (e.g., Fig. 8e–h).

In DJF, change of DOD over Mexico, North Africa,
the Middle East and part of northern China (Fig. 9a) projected by the regression model is similar to
that
projected by CMIP5 models over those dust source regions (Fig. 7a), but with
a greater magnitude. In MAM, a decrease in DOD is projected over a large area
of North Africa (Fig. 9b), which is different from the pattern projected
from the CMIP5 multi-model mean (Fig. 7b). The decrease in DOD over the northern
central US is also different from the overall increase projected by CMIP5
DOD. However, the increase in DOD over the Middle East and the decrease in
DOD over northern China are similar to that of CMIP5 DOD. During JJA and
SON, DOD decreases over the Sahel and northern China but increases over a
belt to the north of the central Sahel and parts of the Middle East (Fig. 9c–d).
The weak increase in DOD over the southern corner of South Africa in
JJA and a slight decrease in SON also have high agreement among the
regression projections (dotted areas in Fig. 9c–d). Changes of DOD over
Australia are very small in all seasons and show little consistency among
the regression projections.

The regression model projection using 16-model output shows very similar
patterns (Fig. S7), largely because the projected
changes of precipitation, surface wind speed, and bareness from the 16-model
ensemble mean are similar to those from the seven-model ensemble mean in dusty
regions (Fig. S9). But there are also some discrepancies
in terms of magnitude and pattern that are revealed in the projected DOD
patterns, e.g., the projected reduction of DOD is greater and more
widespread over northern Asia in MAM if using the 16-model output and the
increase in DOD along the southern edge of the Sahara is weaker in JJA and
SON (Fig. S7 vs. Fig. 9).

Figure 10(a–d) Projected change of DOD in the late half of the 21st
century under the RCP8.5 scenario by the regression model and output from
seven CMIP5 models (same as Fig. 9), as well as contributions from each component,
(e–h) precipitation, (j–i) bareness, and (m–p) surface wind speed.
Dotted areas are regions with sign agreement above
71.4 % among the models. To highlight DOD variations near the source regions, a mask of LAI
≤0.5 (from present-day climatology) is applied.

The contribution of each controlling factor to the total DOD change is shown
in Fig. 10. While changes of bareness over North Africa and northern China
play an important role in DOD change, changes of precipitation, e.g., over
northwestern China in MAM, and surface wind, e.g., over northern North
Africa and the Middle East in DJF and MAM, also play vital roles.

Both projections from the CMIP5 models and those from the regression model
have some uncertainties. The reliability of future projections by CMIP5
models is limited by models' capability of capturing present-day climatology
and the observed connection between DOD and local controlling factors. As
discussed earlier, the overall performance of models is better in those very
dusty regions in the NH, such as North Africa and the Middle East, than
other regions. The multi-model mean also overestimates the connection among
DOD, precipitation, and surface wind and underestimates the influence of
bareness in the present (Fig. 6), which can cast doubts on the projected
variation in DOD in response to climate change.

The uncertainties associated with the regression model are twofold. First,
there are uncertainties associated with the regression model itself. Since the
regression coefficients are derived from observed relationships between DOD
and controlling factors in a relatively short time period, factors
controlling the low-frequency variation in DOD (e.g., decadal variations) may
not be included. Other meteorological factors that could play an important
role in regional dust variability, e.g., nocturnal low-level jets (e.g., Todd
et al., 2008; Fiedler et al., 2013, 2016) and haboobs over Africa (e.g.,
Ashpole and Washington, 2013), are not directly considered in the model. The
influences of anthropogenic land use and land cover change are also not included
in the regression model. Anthropogenic land use and land cover change has been
found to play an important role in long-term dust variability in some
regions (e.g., Neff et al., 2005, 2008; Moulin and Chiapello, 2006; McConnell
et al., 2007), although previous modeling studies found its influences on
future dust emissions to be minor compared to climate change (Tegen et al.,
2004). Thus the projection made by the regression model only reveals the change
of DOD in association with climate change. Second, uncertainties associated
with model-projected change of controlling factors, such as bareness in the US in
JJA as pointed out by Pu and Ginoux (2017), also limit the accuracy of the
results.

Despite these uncertainties, both methods make similar projections,
particularly in some dusty regions: for instance, the DOD pattern over North
Africa in DJF and JJA, an increase in DOD in the central Arabian Peninsula
in all seasons, and a decrease in DOD over northern China from MAM to SON
(Figs. 7, 9).

We examined DOD in seven CMIP5 models with interactive dust emission
schemes. Other important variables that influence the radiative property of
dust, such as the Angström exponent and single-scattering albedo, are also
worth further examination, if these variables are archived. A better
quantification of the radiative forcing of dust may also require an
examination of the size distribution of dust particles, as studies (e.g.,
Kok et al., 2017) found that in current AeroCom models the fraction of coarse dust
particles was underestimated and so was the warming effect of dust. Whether
this is the case in the CMIP5 models is not clear.

Also note that since DOD is an integrated variable, it does not reflect the
vertical distribution of dust aerosols. As pointed out by Huneeus et al. (2016),
dust models with similar performance in simulating AOD may have quite large differences in simulating vertical distribution,
emission, deposition, and surface concentration of dust. An overall
evaluation of dust modeling capability will require detailed examination of
these variables and the life cycle of dust in CMIP5 models in addition to
DOD.

Early studies on future dust projection used offline dust models driven by
climate model output under different scenarios. For instance,
Mahowald and Luo (2003) used an offline dust model and output from the
National Center of Atmospheric Research's coupled Climate System Model (CSM)
1.0 (Boville and Gent, 1998) under the A1 scenario
(Houghton et al., 2001) and projected a decrease in dust
emissions by the end of the 21st century by −20 % to −63 %,
depending on different scenarios. In general, when they included vegetation
change, the projected dust reduction became greater, but including land use
change slightly weakened such reduction. Similarly, Tegen et al. (2004) used output
from ECHAM4, HadCM3, and a dust model (Tegen et al., 2002) to examine the change of dust emission by 2040–2050 and 2070–2080 and
found results were model and scenario dependent, from −26 % to 10 %.
However, including anthropogenic cultivation practices tended to increase
dust emission in both models. They also pointed out that such an influence
from anthropogenic land use was not big enough to overcome the effect of
climate change.

The interactive dust emission schemes and new generations of climate models
used in CMIP5 are likely to provide more reliable projections, but this may
also depend on how changes of dust and its radiative forcing are fed back to
the climate system in the models. While these projections are largely
model dependent, based on our analysis on the DOD climatology in CMIP5
models, the multi-model mean has a better chance to provide a more reliable
projection than individual models.

Here a regression model combined with MODIS DOD is used to identify key
local factors that control the variation in DOD on the interannual timescale. The results are then compared with model output to examine models'
capability of capturing observed connections between DOD and controlling
factors. This method may be applied to other dust model intercomparison
projects as well, such as AeroCom (Huneeus et al., 2011), to help examine
model performance.

Dust aerosol plays an important role in the climate system by directly
scattering and absorbing solar and longwave radiation and indirectly
affecting the formation and radiative properties of cloud. It is thus very
important to understand how well dust is simulated in the state-of-the-art
climate models. While many features and variables are systematically
examined in the CMIP5 multi-model output, we found that to the best of our
knowledge an evaluation of global dust modeling in CMIP5 models is still missing. In this study we examined a key variable associated with dust
radiative effect, dust optical depth (DOD), using seven CMIP5 models with
interactive dust emission schemes and DOD retrieved from MODIS Deep Blue
aerosol products.

We found that the global spatial pattern and magnitude are largely captured
by CMIP5 models in the 2004–2016 climatology, with an underestimation of
global DOD (over land) by −25.2 % in MAM to −6.4 % in DJF. The spatial
pattern is better captured in boreal dusty seasons during MAM and JJA. In
JJA, the simulated zonal mean DOD from the multi-model mean largely resembles
MODIS DOD.

The magnitudes of multi-model mean are closer to MODIS climatology than most
individual models and are largely within ± 1 order of magnitude of
MODIS DOD in the nine regions examined here (North Africa, the Middle East,
northern China, North America, India, southeastern Asia, South Africa, South
America, and Australia; see Fig. 1 and Table 2 for domains). While some
models underestimate DOD in North America and South America by more than 2
orders of magnitude, a few also overestimate DOD in Australia by more than
1 order of magnitude. Both the magnitude and spatial patterns of DOD are
better captured over North Africa and the Middle East than other regions.

The multi-model mean also largely captures the seasonal cycle of DOD in some
very dusty regions, such as North Africa and the Middle East. Seasonal
variations in North America and India are also generally captured by the
multi-model mean, with the modeled DOD peaking at approximately the same
season as in MODIS DOD but not so in northern China and southeastern Asia.
Seasonal cycles in those dusty regions in the Southern Hemisphere are
generally not well captured, with modeled DOD over South Africa and South
America peaking later than that in MODIS DOD but earlier in Australia.

The interannual variations in DOD are not captured by most of the CMIP5
models during 2004–2016. Models also underestimate the constraints from
surface bareness on the variations in DOD and overestimate the influences
from surface wind speed and precipitation in those major dust source
regions. CMIP5-projected change of DOD in the late half of the
21st century (under the RCP8.5 scenario) with reference to historical
conditions (1861–2005) also shows greater influence from precipitation and
surface wind change than from surface bareness. Overall, the multi-model mean
projects a change of DOD over land from −3.8 % in SON to 3.3 % in JJA.

We also provide a projection of future DOD change using a regression model
based on local controlling factors such as surface wind, bareness, and
precipitation (Pu and Ginoux, 2017). This model can largely capture the
interannual variations in MODIS DOD in 2004–2016. The regression model
projects a reduction of DOD in the Sahel in all seasons in the late half of
the 21st century under the RCP8.5 scenario, largely due to a decrease
in surface bareness. DOD is projected to increase over the southern edge of
the Sahara in association with surface wind and precipitation changes except
in MAM, when a reduction of DOD over most of North Africa is projected.
DOD is also projected to increase over the central Arabian Peninsula in all
seasons and to decrease over northern China from MAM to SON.

Despite large uncertainties associated with both projections, we find some
similarities between the two, which adds to the confidence of projected DOD
change in these regions, for instance, changes of DOD over North Africa in
DJF and JJA, an increase in DOD in the central Arabian Peninsula in all
seasons, and a decrease in DOD over northern China from MAM to SON.

BP conceived the study, downloaded and analyzed the data
and wrote the manuscript with input from PG. PG retrieved MODIS DOD data from MODIS
Deep Blue aerosol products. All authors edited and commented on the manuscript.

This research is supported by NOAA and Princeton University's Cooperative
Institute for Climate Science and NASA under grants NNH14ZDA001N-ACMAP and
NNH16ZDA001N-MAP. The authors thank Songmiao Fan and Fabien Paulot for their
helpful comments on an early version of this paper. The insightful comments
from the two anonymous reviewers improved the paper. We also thank the AERONET
program for establishing and maintaining the sun photometer sites used in this
study. We acknowledge the World Climate Research Programme's Working Group on
Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling
groups (listed in Table 1 of this paper) for producing and making available their
model output. For CMIP the U.S. Department of Energy's Program for Climate Model
Diagnosis and Intercomparison provides coordinating support and led the development
of software infrastructure in partnership with the Global Organization for Earth System
Science Portals.

Biases in dust modeling may result in biases in simulating energy budget and regional climate. Output of seven Coupled Model Intercomparison Project Phase 5 (CMIP5) models is examined. Seasonal cycle and spatial pattern of dust optical depth (DOD) in very dusty regions are largely captured by multi-model mean. But observed connections between DOD and local controlling factors such as bareness are not well represented. Future projections by CMIP5 models and a regression model are also analyzed.

Biases in dust modeling may result in biases in simulating energy budget and regional climate....